SeisFusion: 3D Seismic Data Reconstruction Model

แนวคิดหลัก

A novel diffusion model tailored for 3D seismic data reconstruction with guided sampling and constraint incorporation.

บทคัดย่อ

The article introduces a novel diffusion model, SeisFusion, designed for reconstructing complex 3D seismic data. It addresses challenges faced by traditional methods in handling missing traces within seismic data. The proposed model incorporates conditional supervision constraints and a 3D neural network architecture to extend the 2D diffusion model to 3D space. By refining the generation process and incorporating missing data, SeisFusion achieves reconstructions with higher consistency. Through ablation studies, optimal parameter values were determined, showcasing superior accuracy in field datasets and synthetic datasets. The method demonstrates effectiveness in addressing a wide range of complex missing patterns.

สถิติ

Traditional methods struggle with large-scale continuous missing data.
Diffusion models offer comprehensive distribution coverage.
SeisFusion exhibits superior reconstruction accuracy in field datasets.

คำพูด

"Traditional methods struggle to handle large-scale continuous missing data."
"SeisFusion introduces conditional supervision constraints into the diffusion model."
"Our method exhibits superior reconstruction accuracy when applied to both field datasets and synthetic datasets."

ข้อมูลเชิงลึกที่สำคัญจาก

SeisFusion

by Shuang Wang,... ที่ arxiv.org 03-19-2024

https://arxiv.org/pdf/2403.11482.pdf

สอบถามเพิ่มเติม

How does the incorporation of conditional supervision constraints enhance the performance of the diffusion model

The incorporation of conditional supervision constraints enhances the performance of the diffusion model by providing guidance and constraints during the sampling process. By introducing self-supervised constraints, the generated data is aligned with input conditions, ensuring that the reconstructed data maintains consistency with the known data. This helps to avoid uncertainties and conflicts between the probability distribution of generated data and that of the data to be reconstructed. The constraints guide the generation process, leading to more accurate reconstructions with higher consistency compared to traditional convolutional neural networks.

What are the implications of extending the 2D diffusion model to 3D space in seismic data reconstruction

Extending the 2D diffusion model to 3D space in seismic data reconstruction has significant implications for capturing complex distributions in three-dimensional seismic datasets. As seismic data is inherently three-dimensional, focusing solely on two-dimensional slices neglects crucial crossline information. By incorporating a 3D neural network architecture into the diffusion model, it becomes possible to extend its capabilities from 2D to 3D space. This extension allows for better coverage of complex distributions in 3D seismic datasets, resulting in enhanced performance and accuracy in reconstructing complete seismic data.

How can the concept of guided sampling be applied to other fields beyond geophysics

The concept of guided sampling can be applied beyond geophysics to various fields where missing or incomplete data need reconstruction or interpolation. In medical imaging, guided sampling could aid in reconstructing MRI or CT scan images with missing information due to artifacts or noise. In finance, guided sampling could help fill gaps in financial time series datasets for more accurate analysis and forecasting. Additionally, guided sampling could be beneficial in environmental monitoring for filling missing sensor readings or satellite imagery pixels accurately. Overall, guided sampling offers a versatile approach for improving reconstruction tasks across different domains by leveraging existing information as guidance during generation processes.